DBMS > Drizzle vs. IBM Db2 warehouse vs. Kdb vs. Microsoft SQL Server vs. Teradata

System Properties Comparison Drizzle vs. IBM Db2 warehouse vs. Kdb vs. Microsoft SQL Server vs. Teradata

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Cloud-based data warehousing service

High performance Time Series DBMS

Microsofts flagship relational DBMS

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Time Series DBMS
Vector DBMS

Relational DBMS

Secondary database models

Relational DBMS

Document store
Graph DBMS
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	1.37
Rank	#160	Overall
	#74	Relational DBMS

Score	7.71
Rank	#49	Overall
	#2	Time Series DBMS
	#1	Vector DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

www.ibm.com/products/db2/warehouse

kx.com

www.microsoft.com/en-us/sql-server

www.teradata.com

Technical documentation

code.kx.com

learn.microsoft.com/en-US/sql/sql-server

docs.teradata.com

Developer

Drizzle project, originally started by Brian Aker

IBM

Kx Systems, a division of First Derivatives plc

Microsoft

Teradata

Initial release

2008

2014

2000

kdb was released 2000, kdb+ in 2003

1989

1984

Current release

7.2.4, September 2012

3.6, May 2018

SQL Server 2022, November 2022

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

GNU GPL

commercial

free 32-bit version

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

C++

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux
OS X
Solaris
Windows

Linux
Windows

hosted
Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Import/export of XML data possible

yes

Secondary indexes

yes

table attribute 'grouped'

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

with proprietary extensions

yes

SQL-like query language (q)

yes

SQL 2016 + extensions

APIs and other access methods

JDBC

.NET Client API
JDBC
ODBC
OLE DB

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

C
C++
Java
PHP

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

PL/SQL, SQL PL

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

UDFs, stored procedures, table functions in parallel

Triggers

hooks for callbacks inside the server can be used.

yes

with views

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Source-replica replication

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

rights management via user accounts

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
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Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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